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Dataset results
11 results for “Multi-level network”
A multi-level network tool to trace wasted water from farm to fork and backward
<p>NETFLOW - Network-based 13 Evaluation Tool for Food LOss and Waste<br>V 0.1</p> <p>####################################################################################################################################################</p> <p><br>Authors:<br>Francesco Semeria - Politecnico di Torino - francesco.semeria@polito.it<br>Marta Tuninetti - Politecnico di Torino<br>Luca Ridolfi - Politecnico di Torino</p> <p>####################################################################################################################################################</p> <p>CONTENT OF THIS ARCHIVE</p> <p>The listed files contain output data from the NETFLOW tool and assess the impact on water resources of food loss and waste (FLW) for wheat an its main derived products (flour, bran, pasta and bread).</p> <p>In particular, they quantify such impact offering two perspectives: <br> 1. supply-side, from FLW associated to food consumption backwards to the countries of production;<br> 2. utilisation-side, from the countries of production forward to the countries where FLW occurs.</p> <p>It should be noted that the two perspectives allow to identify two different aspects of the FLW issue.</p> <p><br>List of files:</p> <p>data_fig2_ita_supply_vw.xlsx = output data regarding the supply network of Italy.<br>data_fig3_usa_utilisation_vw.xlsx = output data regarding the utilisation network of the United States.<br>data_fig4_global_supply_vw.xlx = output data regarding the global supply network.</p> <p><br>Modelling scripts are currently available upon request.</p>
Dataset: Multi-level network dataset of social-ecological interdependencies in ten Swiss wetlands based on qualitative interviews and quantitative surveys
<p>The dataset originated from quantitative online surveys and qualitative expert interviews with organizational actors relevant to the governance of ten Swiss wetlands from 2019 till 2021. Multi-level networks represent the wetlands governance for each of the ten cases. The collaboration networks of actors form the first level of the multi-level networks and are connected to multiple other network levels that account for the social and ecological systems those actors are active in. 521 actors relevant to the management of the ten wetlands are included in the collaboration networks; quantitative survey data exists for 71% of them. A unique feature of the collaboration networks is that it differentiates between positive and negative forms of collaboration specified based on actors' activity areas. Therefore, the data describes not only if actors collaborate but also how and where actors collaborate. Further additional two-mode networks (actor participation in forums and involvement in other regions outside the case area) are elicited in the survey and connected to the collaboration network. Finally, the dataset also contains data on ecological system interdependencies in the form of conceptual maps derived from 34 expert interviews (3-4 experts per case).</p>
main source codes and files of "Meta-path Based Prioritization of Functional Drug Actions with Multi-Level Biological Networks"
<p>These source codes and their related files are associated the study. "Meta-path Based Prioritization of Functional Drug Actions with Multi-Level Biological Networks"</p> <p>This study is in process of publication.</p>
Data: Metabolic modeling reveals a multi-level deregulation of host-microbiome metabolic networks in IBD
<p>This archive contains all scripts, resource data and results, including intermediate results to reproduce the results for "Metabolic modeling reveals a multi-level deregulation of host-microbiome metabolic networks in IBD". </p>
Development of a Multi-Level Dynamic Model to Measure the Resilience Level of Transportation Infrastructure Networks
<p>The recent increase in disasters is making the largest critical infrastructure system namely the transportation infrastructure system susceptible to unexpected damage. Discontinuation of services provided by transportation infrastructures will create significant societal, economic, and collateral damages. Therefore, this study aims to identify dimensions to measure the resilience of the transportation infrastructures. This study also aims to develop a model to measure the resilience of the transportation infrastructures resilience. To fulfill the aims of this study, a questionnaire was developed which was supported by a comprehensive literature review. 92 valid responses were received and analyzed qualitatively and quantitatively. Statistically significant variables were used to develop a resilience measurement tool. The developed tool will provide relative resilience measures for multiple projects which will help in identifying the most vulnerable segment of the transportation infrastructure network. Exploratory factor analysis (EFA) was performed to identify the constructs and structural equation modeling (SEM) was used to develop the model. Without previous experience in reconstruction works, handling integrated assets becomes very critical. Also, such inexperience makes it difficult to handle emergency resources properly. However, such issues regarding integrated assets can be resolved by investing in locating integrated assets away from the roadways, so if a break in a railroad crossing or utility line occurs or emergency repairs are needed, the impact on the roadway operations can be minimized. To avoid issues related to access to previous disaster data for the roadway this study suggess investing in preparing an interactive online platform for recording and reviewing data related to disasters as well as previous resilience enhancing activities for the roadway with easy access credentials. The findings of this study will support practitioners and decision-makers in investing in the appropriate resilience enhancement activity project for funding and investment.</p>
Wearable Network for Multi-Level Physical Fatigue Prediction in Manufacturing Workers - Dataset
<p>This dataset contains data from 43 subjects following two authentic manufacturing protocols and their self-reported fatigue score. The system employs 6 wearable sensors to continuously track vital and locomotive signs from multiple body locations.</p> <p> </p> <p>The goal of the experiment is to predict fatigue trends in a subject, while they are asked to perform pre-defined manual tasks simulating a manufacturing environment, using data from soft, flexible, wearable sensors and a vision system. The tasks in this study are repetitive and physically exerting involving intricate steps taken in real manufacturing settings. The iterative nature of the tasks facilitates comparative analyses of distinct temporal segments to characterize fatigue. The two manufacturing tasks are (1) Task Composite: Composite Sheet Layup, and (2) Task Harnessing: Wire Harnessing. The task protocol requires the subject to wear sensors to monitor vital and locomotive signs continuously. Additionally, we incorporate a weighted vest to exaggerate the induced fatigue in a reasonable duration for the study to mimic a full shift for a manufacturing worker. Each task consists of two rest periods of 5 minutes each at the start and end, as well as five segments of physical tasks. On average, each task takes a total of 1 hour. Before each data segment, the subject fills out a survey form to indicate their current fatigues as per the Borg scale. </p>
Figure 2 of the paper "Multi-level structure of the First Tuesday communities after the 2000 dot-com crash: A social network analysis of economic actors based on web archives"
<p><span><span><span><span><span><span><span><span>An example of a First Tuesday meeting held</span></span></span></span></span></span><span><span><span><span><span><span> in Riga in December 2001.</span></span></span></span></span></span></span></span></p>
Figure 1 of the paper "Multi-level structure of the First Tuesday communities after the 2000 dot-com crash: A social network analysis of economic actors based on web archives"
<p><span><span><span><span><span><span><span><span>The temporal evolution of the </span></span></span></span></span></span><span><span><span><span><span><span>firsttuesday.com </span></span></span></span></span></span><span><span><span><span><span><span>website </span></span></span></span></span></span><span><span><span><span><span><span>reconstructed from a collection of web archives by using the web cernes approach (Lobbé 2023). The website grows from the center of the figure in 1999</span></span></span></span></span></span><span><span><span><span><span><span>, </span></span></span></span></span></span><span><span><span><span><span><span>then splits into sub-sections. It </span></span></span></span></span></span><span><span><span><span><span><span>was gradually</span></span></span></span></span></span><span><span><span><span><span><span> abandoned after 2004 before being erased in 2010. The blue, green, and orange </span></span></span></span></span></span><span><span><span><span><span><span>sections </span></span></span></span></span></span><span><span><span><span><span><span>represent the sections where the First Tuesday meetings were announced.</span></span></span></span></span></span></span></span></p>
A Social-networking-site(SNS) Based Weight Loss Behavior Intervention- the Effect of Social Network and Multi-level Factors on Weight Loss
ClinicalTrials.gov study NCT06237933. IPD Sharing: NO. Countries: 1. Publications: 0.
Multi-level Support Vector Regression analysis to identify condition-specific regulatory networks
GEO Series GSE20700. Homo sapiens. 6 samples. Type: Expression profiling by array.
Wearable Network for Multi-Level Physical Fatigue Prediction in Manufacturing Workers
<p>This repository contains the datasets from the 'Wearble Network for Multi-Level Physical Fatigue Prediction in Manufacturing Workers' study. The repository has been divided into 2 tasks folders Task Harnessing and Task Composite with 43 participant subfolders. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.